Method and apparatus for measuring temperature of a living body

ABSTRACT

A method and apparatus for measuring the temperature of a living body, providing a predetermined predictive functional formula in which the value of a shape parameter for reflecting the shape of a sensed temperature curve and the value of coefficient parameters for superimposing said prediction function on said sensed temperature curve are unknown. The temperature of a living body is sensed to obtain temperature data for subsequent processing, and elapsed time from start of temperature measurement is measured to obtain elapsed time data. The value of shape parameter is set on the basis of prescribed temperature data, and the value of coefficient parameters are set by solving simultaneous equations composed of a plurality of said predictive functional formula which includes said set value of shape parameter, and in which temperature data at a plurality of different points in time serve as purposive variables and functions of time data at the plurality of points in time serve as explicative variables. Sensed temperature that will prevail at a future time is calculated through prediction processing in accordance with the functional formula specified by said set value of shape parameter and coefficient parameters.

FIELD OF THE INVENTION

This invention relates to a method and apparatus for measuring thetemperature of a living body, and more particularly, to such method andapparatus capable of predicting what a sensed temperature will be at afuture time.

BACKGROUND OF THE INVENTION

In a conventional apparatus, often referred to as an electronic clinicalthermometer, for measuring the temperature of a living body, theapparatus is programmed to incorporate a prediction formula set up toperfectly define temperature rise curves, and a so-called "add-on value"determined by the prediction formula is added to an actually sensedtemperature to obtain an early display of what the equilibriumtemperature should eventually be. To this end, it is required thatconstants (parameters) used in the prediction formula be set to valueswhich will statistically minimize a prediction error. This is done inthe manufacturing process of each electronic clinical thermometer byapplying statistical processing to actual measurement values obtainedfrom a temperature probe used in actually measuring temperature.

It is known that temperature rise curves differ from one individual toanother, and that a temperature rise curve when temperature is sensed inan armpit will differ considerably from that when temperature is sensedorally even for one and the same individual. As a result, an earlydisplay of an accurately predicted equilibrium temperature cannot beobtained in actual practice even if the prediction formula is correctedfor dispersion exhibited by the thermal characteristics of the probe.

An electronic clinical thermometer disclosed in the specification ofJapanese Patent Application Laid-Open (KOKAI) No. 58-225326 solves theabove problem by incorporating a plurality of prediction formulae. Morespecifically, the thermometer is provided beforehand with a plurality ofprediction formulae stipulated by statistical processing based on alarge quantity of measurement results. When temperature is actuallymeasured, condition settings are altered on a trial-and-error basis, asby comparing the rise curve of the temperature being measured and aselected one of the prediction formulae. In other words, the parametersin the selected prediction formula are modified by trial and error tosolve the aforementioned problem encountered in the prior art. However,since the plural prediction formulae must be defined in advance, apractical problem that cannot be avoided is the trouble involved inadjusting for dispersion in the thermal characteristics of thetemperature probes when these are mass-produced. Furthermore, in orderto raise the accuracy of prediction, a large number of predictionformulae having different rise curves must be incorporated in thethermometer in advance. If an improper prediction formula is selectedjust as temperature is starting to rise, moreover, the transitionexhibited by the predicted value may overshoot.

An electronic clinical thermometer disclosed in the specification ofJapanese Patent Application Laid-Open (KOKAI) No. 59-187233 solves thisproblem by setting up a prediction formula which conforms to the risecurve of the actually measured temperature. In other words, use is madeof the fact that a linear relationship (TL=A-τ't) exists between alogarithmic value TL of the time differential of measured bodytemperature and a sampling time t, with A and τ' being determined by aregression method. However, since the logarithmic value TL is notmeasured body temperature per se, an error due to differential andlogarithmic calculations is introduced into the temperature data, andthe error has a major influence on the setting of the constants A andτ'. Moreover, if the measured temperature data include a noisecomponent, the latter affects the predictive results in the manner of anexponential function, causing the predicted values to exhibit a highlyunstable transition.

SUMMARY OF THE INVENTION

Accordingly, an object of the present invention is to provide a methodand apparatus for measuring the temperature of a living body, in whichan accurate early display of temperature is obtained even if temperaturerise curves differ due to variability or dispersion in the thermalcharacteristics of a probe, differences among individuals anddifferences in the region of the body where temperature is sensed.

Another object of the present invention is to provide a method andapparatus for measuring the temperature of a living body, in which astable transition in predicted temperature is obtained even if thesensed temperature contains a noise component.

Still another object of the present invention is to provide a method andapparatus for measuring the temperature of a living body, in whichsensed temperature which will prevail at any future time is predictedeasily and accurately.

Yet another object of the present invention is to provide a method andapparatus for measuring the temperature of a living body, in which anequilibrium temperature value which will prevail in the future uponelapse of an extended period of time is accurately predicted.

A further object of the present invention is to provide a method andapparatus for measuring the temperature of a living body, in which thereliability of a predicted temperature is greatly enhanced by mitigatingthe influence of a fluctuation in temperature change as caused bymovement of the living body undergoing temperature measurement.

A further object of the present invention is to provide a method andapparatus for measuring the temperature of a living body, in which thereliability of a predicted temperature is greatly enhanced by limitingthe specific part of the living body where temperature is sensed, e.g.by limiting the part to the armpit, mouth or rectum.

A further object of the present invention is to provide a method andapparatus for measuring the temperature of a living body, in which avalid prediction display is obtained at a comparatively early stage byaccurately recognizing the shape of a sensed temperature curve, or theshape of the sensed temperature rise, at an early stage.

A further object of the present invention is to provide a method andapparatus for measuring the temperature of a living body, in which theabovementioned objects are attained through a simple construction andsimple data processing without requiring the storage or simultaneousprocessing of a large quantity of temperature data.

According to the present invention, the foregoing objects are attainedby providing a method of measuring the temperature of a living bodycomprising a step of providing a predetermined predictive functionalformula in which the value of a shape parameter for reflecting the shapeof a sensed temperature curve and the value of coefficient parametersfor superimposing said prediction function on said sensed temperaturecurve are unknown, a temperature sensing step of sensing temperature andgenerating temperature data indicative of the temperature sensed, a timemeasurement step of measuring elapsed time from start of temperaturemeasurement and generating time data indicative of the measured elapsedtime, a shape parameter setting step of setting the value of shapeparameter on the basis of prescribed temperature data obtained in thetemperature sensing step, a coefficient parameter setting step ofsetting the value of coefficient parameters by solving simultaneousequations composed of a plurality of predictive functional formula whichincludes said set value of shape parameter, and in which temperaturedata at a plurality of different points in time serve as purposivevariables and functions of time data at the plurality of points in timeserve as explicative variables, and a prediction processing step ofcalculating sensed temperature which will prevail at a future time inaccordance with the predictive functional formula specified by said setvalue of shape parameter and coefficient parameters.

In a preferred embodiment, the shape parameter setting step includessetting the value of shape parameter on the basis of predeterminedtemperature rise slope information, which is based on plural items oftemperature data.

In a preferred embodiment, the shape parameter setting step includessetting the value of shape parameter by detecting a point at which thesensed temperature curve exhibits a first predetermined slope, detectinga second slope S₁ preceding the detected point and a third slope S₂following the detected point, and comparing the second and third slopes.

In a preferred embodiment, the shape parameter setting step includessetting the value of shape parameter α, on the basis of the second slopeS₁ and third slope S₂, in accordance with the following equation:

    α=Q.sub.1 (S.sub.1 /S.sub.2)+Q.sub.2 (S.sub.1 /S.sub.2).sup.n +Q.sub.3

where

n (a constant) <1

Q₁ -Q₃ : constants

In a preferred embodiment, the shape parameter setting step includessetting the value of shape parameter on the basis of plural items oftemperature data at an early stage of temperature measurement followingthe start of measurement.

In a preferred embodiment, the shape parameter setting step includessetting the value of shape parameter α, on the basis of informationX_(k) based on plural items of temperature data T_(k) at respectivepredetermined points in time, in accordance with the following equation:##EQU1## where where D₀ -D₅ : constants

X₀ -X₃ : T₀ -T₃

X₄ =(X₃ -X₀)/(X₁ -X₀)

In a preferred embodiment, the coefficient parameter setting stepincludes setting the value of coefficient parameters A₀, A₁ by solvingthe following simultaneous equations with two unknowns:

    T(t.sub.1)=A.sub.0 +A.sub.1 /t.sub.1.sup.α

    T(t.sub.2)=A.sub.0 +A.sub.1 /t.sub.2.sup.α

on the basis of temperature data T(t₁), T(t₂) at two different points intime and time data t₁, t₂ respectively indicative of the points in timeat which the temperature is sensed.

In a preferred embodiment, the coefficient parameter setting stepincludes using, as the temperature data at the two different points intime, temperature data in the vicinity of measurement starting time andtemperature data at a present point in time.

In a preferred embodiment, the prediction processing step includescalculating a sensed temperature T_(p) (t_(D)), which will prevail at afuture time t_(D), in accordance with the following equation:

    T.sub.p (t.sub.D)=A.sub.0 +A.sub.1 /t.sub.D.sup.α

based on a prediction function specified by the value of shape parameterα and coefficient parameters A₀, A₁.

According to the present invention, the foregoing objects are attainedby providing an apparatus for measuring the temperature of a living bodycomprising memory means for storing a predetermined predictivefunctional formula in which the value of a shape parameter forreflecting the shape of a sensed temperature curve and the value ofcoefficient parameters for superimposing said prediction function onsaid sensed temperature curve are unknown, temperature sensing means forsensing temperature and generating temperature data indicative of thetemperature sensed, time measuring means for measuring elapsed time fromstart of temperature measurement and generating time data indicative ofthe measured elapsed time, shape parameter setting means for setting thevalue of shape parameter on the basis of predetermined temperature dataoutputted by the temperature sensing means, coefficient parametersetting means for setting the value of coefficient parameters by solvingsimultaneous equations composed of a plurality of predictive functionalformula which includes the value of shape parameter set by the shapeparameter setting means, and in which temperature data at a plurality ofdifferent points in time serve as purposive variables and functions oftime data at the plurality of points in time serve as explicativevariables, and prediction processing means for calculating sensedtemperature which will prevail at a future time in accordance with thepredictive functional formula specified by said set value of shapeparameter and coefficient parameters.

In a preferred embodiment, the temperature sensing means includes peakholding means for successively detecting peak levels of sensedtemperature and for holding and outputting the detected peak levels.

In a preferred embodiment, the temperature sensing means includes peakholding means for successively detecting peak levels of temperaturesensed at a predetermined period and for holding and outputting thedetected peak levels, and averaging means for obtaining and outputting arunning average value of plural peak levels held by the peak holdingmeans.

In a preferred embodiment, the shape parameter setting means sets thevalue of shape parameter on the basis of predetermined temperature riseslope information, which is based on plural items of temperature data.

In a preferred embodiment, the shape parameter setting means sets thevalue of shape parameter on the basis of plural items of temperaturedata at an early stage of temperature measurement following the start ofmeasurement.

In a preferred embodiment, the coefficient parameter setting means setsthe value of coefficient parameters A₀, A₁ by solving the followingsimultaneous equations with two unknowns:

    T(t.sub.1)=A.sub.0 +A.sub.1 /t.sub.1.sup.α

    T(t.sub.2)=A.sub.0 +A.sub.1 /t.sub.2.sup.α

on the basis of temperature data T(t₁), T(t₂) at two different points intime and time data t₁, t₂ respectively indicative of the points in timeat which temperature is sensed.

In a preferred embodiment, the coefficient parameter setting meansincludes using, as the temperature data at the two different points intime, temperature data in the vicinity of measurement starting time andtemperature data at a present point in time.

In a preferred embodiment, the prediction processing means calculates asensed temperature T_(p) (t_(D)), which will prevail at a future timet_(D), in accordance with the following equation:

    T.sub.p (t.sub.D)=A.sub.0 +A.sub.1 /t.sub.D.sup.α

based on a prediction function specified by the set value of shapeparameter α and coefficient parameters A₀, A₁.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating the basic construction of anelectronic clinical thermometer bodying the present invention;

FIGS. 2(A) and 2(B) are block diagrams illustrating the specificconstruction of a first embodiment of the electronic clinicalthermometer according to the invention;

FIGS. 3(A)-3(E) are flowcharts showing temperature sensing processingexecuted in the first embodiment of the electronic clinical thermometer;

FIG. 4(A) is a flowchart showing shape recognition processing in thefirst embodiment;

FIG. 4(B) is a flowchart showing curve analysis processing in the firstembodiment;

FIG. 4(C) is a flowchart showing prediction processing in the firstembodiment;

FIG. 5 is a timing chart illustrating temperature sensing processingexecuted in the first embodiment of the electronic clinical thermometer;

FIG. 6 is a graph showing a plurality of typical temperature rise curvesselected by actual measurement and statistical processing;

FIG. 7 is a conceptual view showing a heat conduction model of a bodytemperature measurement system;

FIG. 8 is a graph showing an average temperature rise curve whentemperature is sensed in an armpit by the electronic clinicalthermometer of the first embodiment;

FIG. 9 is a graph showing a temperature rise curve, which fluctuates dueto body movement, when temperature is sensed in an armpit by theelectronic clinical thermometer of the first embodiment;

FIG. 10 is a graph showing a temperature rise curve, which indicates avery gentle rise, when temperature is sensed in an armpit by theelectronic clinical thermometer of the first embodiment;

FIGS. 11(A) and 11(B) are block diagrams illustrating the specificconstruction of a second embodiment of the electronic clinicalthermometer according to the invention;

FIGS. 12(A)-12(C) are flowcharts showing shape recognition processing,curve analysis processing and prediction processing executed in thesecond embodiment of the electronic clinical thermometer;

FIG. 13 is a graph showing the transitions of sensed temperature dataT₀, peak data T_(OP) and running average value T_(OA) of the peak datain the second embodiment;

FIG. 14 is a graph showing the relationship between an optimum value ofa shape parameter and a variable (S₁ /S₂) for deciding the shapeparameter in the second embodiment;

FIGS. 15(A) and 15(B) are block diagrams illustrating the specificconstruction of a third embodiment of an electronic clinical thermometeraccording to the invention;

FIGS. 16(A)-16(E) are flowcharts illustrating a temperature sensingprocess executed in the third embodiment of the electronic clinicalthermometer;

FIG. 17 is a graph showing a plot of plural items of sensed temperaturedata at predetermined times after the start of temperature measurementin the third embodiment;

FIGS. 18 and 19 are graphs showing temperature sensed in an armpitplotted against elapsed measurement time in the electronic clinicalthermometer of the second embodiment; and

FIGS. 20 through 22 are graphs showing orally sensed temperature plottedagainst elapsed measurement time in the electronic clinical thermometerof the third embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Preferred embodiments of the invention will now be described in detailwith reference to the accompanying drawings.

First Embodiment Construction

FIg. 1 is a block diagram illustrating the basic construction of anelectronic clinical thermometer embodying the present invention. Thethermometer basically comprises a temperature measurement section 1, aprediction processing section 2, and a display section 3.

The temperature measurement section 1 senses the temperature at a partof a living body at a predetermined period and outputs temperature dataT, which represents the sensed temperature, over a line 103.

The prediction processing section 2 incorporates a predeterminmedpredictive functional formula in which the value of a shape parameterfor reflecting the shapes of a sensed temperature curve and the value ofcoefficient parameters for superimposing said prediction function onsaid sensed temperature curve are unknown. Before measurement starts,the prediction processing section 2 monitors the sensed temperature dataT from the measurement section 1 to determine whether predeterminedconditions for starting measurement have been satisfied. Oncemeasurement has started, the section 2 monitors both the sensedtemperature data T from the measurement section 1 and time data t froman internal function which keeps track of elapsed measurement time. Theprocessing section 2 is further adapted to detect a point at which thedetected temperature data T indicate a first predetermined slope, detectsecond and third slopes preceding and following the detected point,respectively, and compare the second and third slopes, thereby settingthe value of the shape parameter of the prediction function. Theprocessing section 2 then proceeds to solve two simultaneous equations,with too unknowns, of the prediction function. In the simultaneousequations, which include the set value of shape parameter, sensedtemperature data at two points serve as purposive variables andfunctions of time data at the two points in time serve as explicativevariables. Solving the two simultaneous equations sets the value ofcoefficient variables of the prediction function. Next, the processingsection 2 calculates sensed temperature which will prevail at a futuretime t_(D) in accordance with the prediction function specified by theset value of shape parameter and coefficient parameters. The processingsection 2 outputs the results of the calculation, namely a predictedtemperature T_(p) (t_(d)), over a line 121.

The display section 3 numerically displays the predicted temperatureT_(p) (t_(D)), which is calculated successively with the passage oftime.

FIGS. 2(A) and 2(B) are block diagrams showing the construction of theelectronic clinical thermometer of the first embodiment in greaterdetail.

As shown in FIGS. 2(A) and 2(B), the temperature measurement section 1includes a temperature-responsive element 4 such as a thermister, and atemperature measuring circuit 5. In accordance with a data samplingsignal C₁ having a predetermined period received from the predictionprocessing section 2 via a line 102, the temperature measurement circuit5 samples an analog voltage signal 101, which conforms to thetemperature sensed by the element 4, and converts the signal intodigital temperature data T outputted on lines 103, 104.

The prediction processing section 2 includes data read-in means 6, timemeasuring means 7, measurement control means 8, memory means 9 forstoring temperature data, shape recognition means 10 for recognizing theshape of a sensed temperature curve, curve analyzing means 11 foranalyzing the sensed temperature curve, prediction arithmetic means 12,and a data selector 13. It should be noted that the functional blocks 6through 13 shown in FIGS. 2(A) and 2(B) can be implemented by having amicrocomputer (CPU) execute the programs shown in FIGS. 3(A) through3(E) and FIGS. 4(A) through 4(C), which are stored in a memory (ROM orRAM), not shown. The arithmetic processing section 2 is also providedwith a buzzer 14, which emits a sound to inform the user of the factthat a valid temperature prediction has been made.

The measurement control means 8 controls the overall operation of theelectronic clinical thermometer. Prior to the start of temperaturemeasurement, the control means 8 causes the temperature measuringcircuit 5 to generate the temperature data T at a rate of e.g. once perfive seconds to save power, monitors the temperature data constantly vialine 104, and determines whether predetermined measurement startingconditions have been satisfied. For example, this means determiningwhether the data T represent a temperature higher than a certaintemperature value, and whether the amount of temperature change isgreater than a certain value. When these conditions are satisfied, thecontrol means 8 outputs a control signal C₂ over a line 105 to activatesuch functional blocks as the data read-in means 6, time measuring means7 and memory means 9, whereby measurement is started. After measurementstarts, the control means 8 receives, via a line 100, a clock signalCLOCK having a period of e.g. 1 sec generated by a CPU. The variousblocks operate, in a manner described later, in accordance with timerinterrupt processing, described below, provided so as to respond to theclock signal.

The measurement control means 8 is provided with a group of registersstoring decision constants necessary for measurement control to proceed.The registers include a register t_(M) for storing a value (e.g. 600sec) of elapsed measurement time, beyond which it would be meaninglessto continue predictive calculations when actually measuring bodytemperature; a register t_(A) for storing a time (e.g. 1 sec), in thevicinity of a measurement starting point, as a time for sampling oneitem of temperature data necessary for setting the parameters of theprediction function; a register k storing an amount of temperatureincrease (e.g. 0.15° C. in 8 sec) for detecting the aforementioned pointat which the sensed temperature curve indicates the first predeterminedslope; a register q storing an allowable value (e.g. 0.02° C.), which isused in deeming that a prediction is valid, regarding the absolute valueof a difference between predicted temperatures calculated at apredetermined time interval (e.g. 8 sec); and a register DT for storingtime period information (e.g. 16 sec) representing time from the pointat which the first predetermined slope is detected to the point at whichthe third slope is detected.

The sensed temperature data T are outputted on line 103 at the same timethat the measurement control means 8 outputs the sampling signal C₁having the period of one second via line 102. The data read-in means 6reads the sensed temperature data T into the prediction processingsection 2, and is capable of storing plural item of successivetemperature data T while updating the same in FIFO (first-in first-out)fashion. The data read-in means 6 has an output terminal from which itis possible to obtain a running average value T_(av) of the plural itemsof sensed temperature data. If such an arrangement is adopted, the itemsof temperature data T can be averaged so that the predicted temperaturewill exhibit a stabler transition.

The time measuring means 7 clocks elapsed time from the start oftemperature measurement and outputs, via line 107, elapsed time datet_(i) representing the elapsed time. After temperature measurementstarts, the time measuring means 7 counts up the 1 sec signal outputtedby the measurement control means via line 105, thereby keeping track ofelapsed time from the start of temperature measurement.

The memory means 9 constantly stores nine items of sensed temperaturedata, including the most recent, while successively performing ashift-in/shift-out of the sensed temperature data T_(O), read in by theread-in means 6, from a register T8 to a register T_(O) of the memorymeans.

The shape recognition means 10 is adapted to grasp, through a simple andeffective method, the shape of the rising part of the sensed temperaturecurve observed at an early stage of body temperature measurement, andthe purpose thereof is to set the value of the prediction function shapeparameter α early in the temperature measurement process.

The concept of the shape parameter setting procedure will now bedescribed with reference to FIG. 6.

FIG. 6 is a graph showing a plurality of typical temperature rise curvesselected by actual measurement and statistical processing. Elapsedmeasurement time t (sec) is plotted along the horizontal axis, and thesensed temperature data T (° C) are plotted along the vertical axis. Asis apparent from the graph, the temperature rise curves all exhibit afairly steep rising characteristic in the vicinity of measurementstarting time, then a first slope (from time tB₁ to time tB₆ in thegraph), and then a gently rising characteristic.

The shape of such temperature rise curves can be expressed by an element(1/.t_(i).sup.α in the second term on the right-hand side of thefollowing prediction formula in accordance with the invention:

    T.sub.o (t.sub.i)=A.sub.0 +A.sub.1 /t.sub.i.sup.α

In other words, if the value of shape parameter α is suitably selected,the shape of any one of the temperature rise curves in FIG. 6 can beexpressed. Values of the shape parameter α corresponding to thetemperature rise curve shapes are shown in FIG. 6 and serve asillustrative examples. It will be understood from the graph that thelarger the value of α in the vicinity of measurement start, the steeperthe rising characteristic will be, and that the smaller the value of α,the gentler the rising characteristic will be. However, a sharpdistinction cannot be drawn among a plurality of temperature rise curvesbased solely on whether the rising characteristic near the beginning ofmeasurement is steep or gentle.

Accordingly, the shape recognition means 10 is adapted to detect thepoint (the shoulder portion in FIG. 6) at which the temperature risecurve exhibits the first predetermined slope, a second slope of arelatively steeper portion of the curve preceding the abovementionedpoint, and a third slope of a relatively gentler portion of the curvefollowing the abovementioned point, and to compare the second and thirdslopes to distinguish among the plurality of temperature rise curves.For a temperature rise curve associated with the parameter α=0.6, by wayof example, the comparison will show that the second slope is very steepand the third slope very gentle. For a temperature rise curve associatedwith the parameter α=0.1, on the other hand, the comparison will showthat the second slope is relatively gentle as well as the third slope.Accordingly, if the second and third slopes flanking the shoulderportion are examined, more useful information for distinguishing amongthe plurality of temperature rise curves can be obtained. Thus, it canbe expected that employing the ratio of the two slopes (secondslope/third slope) will greatly clarify the differences among the curveshapes, and that the value of the ratio will be proportional to thevalue of the shape parameter α.

The shape recognition means 10 can be adapted to set the value of shapeparameter α by using another method simpler than that described above.For example, it is possible to express the value of shape parameter α bythe slope of a temperature curve at a predetermined times or by pluralitems of temperature data at predetermined times following the start ofmeasurement. More specifically, the shape recognition means 10 can beadapted to set the value of the shape parameter α in accordance with avariable or set of variables having a high correlation with respect tothe value of the shape parameter α.

Thus, in accordance with the concept described above, the shaperecognition means 10 in the present embodiment sets the value of theshape parameter α based on the sensed temperature at an early stage ofbody temperature measurement.

The shape recognition means 10 includes a group of registers for storinginformation necessary to set the value of shape parameter α. Theseregisters include a register T_(A) for storing temperature data T_(A)sensed at a comparatively early time t_(A) after the start oftemperature measurement (e.g. a point in time 1 sec after the start ofmeasurement); a register T_(B) for storing sensed temperature data T_(B)which prevails at a point in time where the measurement control means 8detects the first slope (shoulder); a register t_(B) for storing timet_(B) which has elapsed up to said point in time; a register S₁ forstoring a sensed temperature difference value S₁ =T_(B) -T_(A) ; aregister S₂ for storing a sensed temperature difference value S₂=T8-T_(B), where T8 is the temperature data prevailing a predeterminedtime DT (e.g. 16 sec) after detection of the shoulder; a register α forstoring the shape parameter, which is obtained by performing acalculation expressed by the equation α=(S₁ /S₂)×Q₁ +Q₂ ; a register Q₁for storing the constant Q₁ (e.g. 0.042); and a register Q₂ for storingthe constant Q₂ (e.g. -0.128).

The curve analyzing means 11 employs a prediction function, whichincludes the value of shape parameter α set by the shape recognitionmeans, to set the value of coefficient parameters A₀, A₁ of theprediction function. Specifically, the curve analyzing means 11 obtainsthe value of coefficient parameters A₀, A₁ by solving the followingsimultaneous equations:

    T.sub.O (t.sub.1)=A.sub.0 +A.sub.1 /t.sub.1.sup.α

    T.sub.O (t.sub.2)=A.sub.0 +A.sub.1 /t.sub.2.sup.α

on the basis of temperature data T_(O) (t₁) in the vicinity of themeasurement starting point, temperature data T_(O) (t₂) at a presentpoint in time, and time data t₁, t₂ indicative of the correspondingpoints in time at which the temperatures were sensed. The value ofcoefficient parameters A₀, A₁ thus set, along with the already set thevalue of shape parameter α, uniquely specify the aforementionedprediction formula.

In order to examine the validity of the specified prediction formula atall times, the curve analyzing means 11 also sets the value ofcoefficient parameters A₀ ', A₁ ', which prevailed e.g. eight secondsearlier, by using sensed temperature data which prevailed eight secondsprior to the present as the present temperature data T_(O) (t₂). To thisend, the curve analyzing means 11 is provided with registers A₀, A₁ forstoring the value of coefficient parameters A₀, A₁, which are obtainedby using the presently prevailing sensed temperature data T8 as thepresently prevailing sensed data T_(O) (t₂), and registers A₀ ', A₁ 'for storing the value of coefficient parameters A₀ ', A₁ ', which areobtained by using the sensed temperature data T0 that prevailed eightseconds prior to the present as the presently prevailing sensed dataT_(O) (t₂).

The prediction arithmetic means 12 uses the presently prevailingprediction equation specified by the curve analyzing means 11 tocalculate a sensed temperature value which will prevail in the future,preferably at any desired future time. More specifically, in accordancewith the prediction function specified by the set value of shapeparameter α and coefficient parameters A₀, A₁, a predicted value T_(p)(t_(D)) of sensed temperature which will prevail at a future time t_(D)is calculated by using the following equation:

    T.sub.p (t.sub.D)=A.sub.0 +A.sub.1 /t.sub.D.sup.α

This item of predicted temperature data T_(p) (t_(D)) is outputted overa line 120.

Further, in accordance with the prediction function specified by thevalue of coefficient parameters A₀ ', A₁ ' prevailing eight secondsearlier, the prediction arithmetic means 12 similarly calculates apredicted value T_(p) (t_(D))' of sensed temperature which will prevailat future time t_(D) by using the following equation:

    T.sub.p (t.sub.D)'=A.sub.0 '+A.sub.1 /t.sub.D.sup.α

This item of predicted temperature data T_(p) (t_(D))' is outputtedtogether with the aforementioned predicted temperature data T_(p)(t_(D)) to the measurement control means 8 via a line 119. Themeasurement control means 8 compares the items of predicted temperaturedata T_(p) (t_(D)), T_(p) (t_(D))' to judge the validity (consistency)of the predicted value.

The predictive arithmetic means 12 is provided with a group of registersfor storing information needed to supply a predicted temperature. Theseregisters include a register T_(p) for storing temperature T_(p)predicted from the present time onward, a register for storingtemperature T_(p) ' from eight seconds earlier, and a register t_(D) forstoring future time t_(D) (e.g. the time which will prevail upon elapseof ten minutes).

Upon passage of a period of time so long that continuing predictivecalculations will be meaningless in an actual body temperaturemeasurement, the data selector 13, which comprises switching means,terminates the early display based on the predicted temperature T_(p)(t_(D)) and instead switches over to a direct display based on thetemperature data T_(O). Since the data selector 13 is connected to theprediction arithmetic means 12 until it is judged that predeterminedprediction terminating conditions are satisfied after the start ofmeasurement, the display section 3 displays the predicted temperatureT_(p) (t_(D)).

Prediction Principle

The principle of operation for predicting temperature in accordance withthe invention will now be described.

By performing a theoretical analysis of heat conduction in a bodytemperature measurement system, the inventor has estimated the shape ofa temperature rise curve of a temperature probe when body temperature ismeasured. Specifically, the analytical method entails using a model of abody temperature measurement system of the type shown in FIG. 7, by wayof example, dividing the measurement system into three regions, namely aprobe region, skin region and subcutaneous tissue region, and assumingthat the temperature distribution of each region is uniform in the bodytemperature measurement process. In other words, each region is treatedconceptually as being of an infinitesimal volume. With regard to thesubcutaneous tissue region, however, the thermal capacity is assumed tobe infinity. It should be noted that the terms "skin" and "subcutaneoustissue" are used for the sake of convenience since the living body isassumed to be a two-layer model; these do not strictly correspond to theactual structure of a living body. Furthermore, by dividing the systeminto a greater number of parts in accordance with future developments,it will be possible to improve the model to more closely resemble aliving body if this is necessary.

In the measurement system model of FIG. 7, let h₁ represent the thermalconductivity between the probe and skin, A₁ the area of the interface,h₂ the thermal conductivity between the skin and subcutaneous tissue,and A₂ the area of the interface. Further, on the assumption that thethermal capacity of the subcutaneous tissue is infinity, the temperatureof the subcutaneous tissue will be a constant value T_(sat) with respectto time. Thus, the amount of heat absorbed by the probe from the skinafter the probe is brought into contact with a part of the living bodyat which temperature is to be measured is equal to an amount of increasein the internal energy of the probe. Therefore, the following equationholds: ##EQU2##

Similarly, the amount of heat absorbed by the skin from the subcutaneoustissue and the probe is equal to an amount of increase in the internalenergy of the skin. Therefore, the following equation holds: ##EQU3##where T_(p), P_(p), C_(p), V_(p) : temperature, density, specific heatand volume of probe

T_(s), P_(s), C_(s), V_(s) : temperature, density, specific heat andvolume of skin

T_(sat) {=T_(p) (∞)}: subcutaneous tissue temperature=equilibriumtemperature

If the simultaneous linear differential equations comprising Eqs. (1)and (2) are solved, then the following equation is obtained: ##EQU4##where ##EQU5##

Since Eq. (3) is a higher order linear differential equation, it can besolved using a Laplace transformation. That is, using ##EQU6## andcalculating each term, we have ##EQU7## where ##EQU8##

Solving the above for T_(p), we obtain ##EQU9##

Using the solution s² +(K₁ -K₂ +K₃)s+K₁ K₃ =0 for m₁, m₂, we have##EQU10##

When m₁ ≠m₂ holds, we have the following from Eq. (4): ##EQU11##

Since it is known that e^(kx) =1/(s-k), an equation involving T_(p) (t)is obtained as follows:

    T.sub.p (t)=T.sub.sat +M.sub.1 e.sup.m2t +M.sub.2 e.sup.m2t(5)

where ##EQU12##

When m₁ =m₂ holds, we have the following from Eq. (4): ##EQU13##

Since it is known that e^(kx) =1/(s-k), Xe^(kx) =1/(s-K)², an equationinvolving T_(p) (t) is obtained as follows:

    T.sub.p (t)=T.sub.sat +M.sub.3 e.sup.mlt +M.sub.4 te.sup.mlt(6)

where

M₃ =C₀ -T_(sat)

M₄ =C₁ -m₁ C₀ +3m_(l) T_(sat)

Thus, theoretical equations representing the temperature rise curve of aprobe are as given by Eqs. (5) and (6).

In Eqs. (5) and (6), m₁, m₂ and M₁, M₄ are given as functions of variousphysical quantities contained in a body temperature measurement systemincluding physical values (density, specific heat, volume, etc.) of theprobe and skin, and these values vary from one thermometer to anotherand with every temperature measurement. Accordingly, it is required thatm₁, m₂, M₁, M₄ be set on the basis of the temperature data sensed by theprobe when a measurement is taken.

Electronic clinical thermometers include those of the type in whichafter the probe is brought into contact with the part of the body to bemeasured, the temperature data do not begin to be read until the probesenses a predetermined temperature, by way of example. For electronicclinical thermometers such as these, it is convenient to transform Eqs.(5) and (6) into the following equations:

    T.sub.p (t)=T.sub.sat +Pe.sup.mlt +Qe.sup.m2t              (7)

    T.sub.p (t)=T.sub.sat +Re.sup.mlt +Ste.sup.mlt             (8)

where

P=M₁ e^(ml)·Δt

Q=M₂ e^(m2)·Δt

R=M₃ e^(ml)·Δt+M₄ ·Δte^(ml)·Δt

S=M₄ e^(ml)·Δt

In the above, Δt represents elapsed time from the moment the probe iscontacted with the body until the start of measurement, and t representstime where measurement starting time is taken as being t=0.

If m₁, m₂ are taken as being fixed values in Eq. (7), then T_(sat), P,and Q can be obtained with relative ease by regression analysis or bysolving the simultaneous equations using temperature data sensed in atime series when a measurement is taken. However, m₁, m₂ vary with eachmeasurement due differences in the individual

undergoing measurement or a difference in measurement conditions.Moreover, the object of the present invention is to find the optimumprediction function by incorporating all of these variable elementsevery time a measurement is taken, thereby to perform a temperatureprediction having a high degree of universality. Though it ismathematically possible to obtain m₁, m₂, T_(sat), P, and Q by solvingthe simultaneous equation (7) using temperature data sensed when ameasurement is taken, the results would be highly unstable due to thecombined effect of (1) the fact that a noise component is contained inthe sensed temperature data and (2) the fact that Eq. (7) includesexponential terms.

Accordingly, the following equation is obtained by subjecting Eq. (7) toa Taylor expansion: ##EQU14## Deleting terms of the fourth degree onwardgives the following equation:

    T.sub.p (t)=A.sub.0 +A.sub.1 /t+A.sub.2 /t.sup.2 +A.sub.3 /t.sup.3(10)

The foregoing will also hold in similar manner for Eq. (8).

The inventor has previously proposed an electronic clinical thermometerin which coefficient parameters A₀ through A₃ of a prediction functionare set by solving the following simultaneous equation with fourunknowns:

    T.sub.O (t.sub.i)=A.sub.0 +A.sub.1 /t.sub.i +A.sub.2 /t.sub.i +A.sub.2 /t.sub.i.sup.2 +A.sub.3 /t.sub.i .sup.3 (i=0-3)

on the basis of four items of discretely sampled temperature data T_(O)(t_(i)) and time data t_(i) indicative of time at each sampling, usingEq. (10). Since the preceding temperature data include all physicalconditions, the coefficient parameters of the prediction function can beset based on a correlation function between these temperature data andtime data, and a tentative optimum prediction function can be specified.By using the specified prediction function, the temperature at thefuture time t_(D) can be calculated in accordance with the followingequation:

    T.sub.p (t.sub.D)=A.sub.0 +A.sub.1 /t.sub.D +A.sub.2 /t.sub.D.sup.2 +A.sub.3 /t.sub.D.sup.3

This method has a high degree of universality and provides a stableprediction. Even though four items of temperature data are used in theabove case, however, it is necessary that the four items of data beextracted discretely so as to cover the full scale of the latest sensedtemperature curve at all times in order to specify the tentative optimumprediction function every time. To this end, therefore, the old sensedtemperature data cannot be discarded, so that a sensed temperature datamemory having a very large storage capacity is required. Though therequired memory capacity can be reduced somewhat if the number of termson the right-hand side of Eq. (10) are reduced, diminishing the numberof terms excessively results in a prediction function having anexcessively gentle or sluggish rise characteristic. This makes itimpossible to obtain an effective early display of temperature.

Therefore, in accordance with the invention, the prediction function ofEq. (10) is transformed and the following equation is employed toachieve both a reduction in required memory capacity and a valid earlydisplay of temperature:

    T.sub.p (t)=A.sub.0 +A.sub.1 /t.sup.α

Operation

FIGS. 3(A)-3(E) and FIGS. 4(A)-4(C) are flowcharts showing temperaturesensing processing executed by the electronic clinical thermometer ofthe first embodiment. FIG. 5 is a timing chart illustrating temperaturesensing processing executed by the electronic clinical thermometer ofthe first embodiment.

In FIG. 3(A), the first step of the flowchart is a start step S100 atwhich electric power is supplied to the electronic clinical thermometer.This is followed by a temperature measurement step S101, at which thetemperature measurement section 1 and measurement control means 8 areactivated to perform a comparatively rough temperature measurement.Specifically, the measurement control means 8 causes the temperaturemeasuring circuit 5 to sense temperature at a rate of e.g. once per fiveseconds in order to save power, and monitors the sensed temperature dataT. Next, steps S102, S103 call for a decision as to whether bodytemperature measurement based on the prediction method should start. Itis determined at the step S102 whether the sensed temperature hasexceeded a predetermined temperature T_(h), e.g. 30° C., and it isdetermined at the step S103 whether the rate of temperature rise is noless than 0.1° C. per second. In actuality, this step calls for adecision as to whether the thermometer has been placed in an armpit orin the mouth. If the conditions of steps S102, S103 are satisfied, theprogram proceeds to a step S104, at which various program switchesSW1-SW4 for measurement control are cleared. Next, a step S105 calls forthe time measuring means to be cleared and started via line 105. Inother words, a time measuring counter in the time measuring means 7 isreset (to a count corresponding to the elapsed measurement time t0 inFIG. 5) and clocking of elapsed measurement time is started. The dataread-in function of the data read-in means 6 is activated via line 105at a step S106, and the data storage function of the memory means 9 isactivated at a step S107. After execution of these initial setting andcontrol operations, the timer interrupt function for a timer interruptat a rate of e.g. once per second is activated at a step S108, and theCPU executes an idle routine IDLE at a step S109 to await the occurrenceof the timer interrupt.

When a timer interrupt is generated at step S109 in FIG. 3(A), theprogram proceeds to a step S200 in FIG. 3(B). A step S201 in thisflowchart disables the timer interrupt function in order that a seriesof subsequent processing steps may be executed. These steps include astep S202, at which the measurement control means 8 checks whether t_(i)>t_(M) (e.g. 600 sec) holds. Since t_(i) >t_(M) will not hold at theinstant measurement starts, a NO answer is received at step S202 and theprogram proceeds to a step S203, at which it is checked whether SW1=1holds. SW1 is a switch for processing in which temperature data sensedin the vicinity of the start of measurement are stored in memory. IfSW1=1 does not hold, the program proceeds to a step S220 in FIG. 3(D).Then the relation t_(i) =t_(A) is checked at a step S220. The registert_(A) in the measurement control means 8 stores the elapsed timeconstant t_(A) (e.g. 1 sec) indicating elapsed time in the vicinity ofthe start of measurement. This corresponds to the timing t_(A) in FIG.5. If t_(i) =t_(A) does not hold, the program returns to the step S108in FIG. 3(A). However, since the condition t_(i) =t_(A) is satisfied inthe present embodiment, a YES answer is received at the step S220 andthe program proceeds to a step S221, at which the sensed temperaturedata T8 prevailing at the present time is stored in the register T_(A),and then to a step S222, at which SW1 is set to logical "1". The switchSW1 remains at logical "1" from this point onward.

When SW1=1 is found to hold at the step S203, the program proceeds to astep S204, at which it is checked whether SW2=1 holds. SW2 is a switchfor processing in which the first predetermined slope is detected. If aNO answer is received at the step S204, it is determined at a step S230whether ↑T8-T0↑< k holds. If this inequality does not hold, this meansthat the first slope cannot yet be detected, so that the program returnsto the step S108 in FIG. 3(A). When ↑T8-T0↑< k is eventually satisfied,however, the program proceeds to a step S231, at which S₁ =T8-T_(A) isstored in memory, where S₁ is the difference between the two items ofsensed temperature data. As shown in FIG. 5, the difference value S₁=T8-T_(A) indirectly represents the second slope. Next, a step S232calls for the presently prevailing sensed temperature data T8 to bestored in the register T_(B), the next step S233 calls for the datat_(i) indicative of elapsed measurement time up to the present to bestored in the register t_(B), and a step S234 calls for the switch SW2to be set to logical "1". The switch SW2 remains at logical "1" fromthis point onward.

When SW2=1 is found to hold at the step S204, the program proceeds to astep S205, at which it is checked whether SW3=1 holds. SW3 is a switchfor shape recognition processing applied to the sensed temperaturecurve, which is done after waiting a predetermined time DT (e.g. 16 sec)following detection of the first slope. When SW3=1 is found not to hold,the program proceeds to a step S240 in FIG. 3(E), at which it isdetermined whether t_(i) =t_(B) +DT holds. If the answer here is NO,then the program returns to the step S108 in FIG. 3(A) until thecondition t_(i) =t_(B) +DT is satisfied. When this condition iseventually satisfied, shape recogition processing is executed at a stepS300. This is time t_(c) in FIG. 5.

Shape Recognition Processing

FIG. 4(A) is a flowchart illustrating shape recognition processing inaccordance with the first embodiment of the invention. A step S301 callsfor a value T8-T_(B), which is the difference between items of sensedtemperature data, to be stored in the register S₂. The difference valueT8-T_(B) indirectly represents the third slope in FIG. 5. Next, a stepS302 calls for the value of shape parameter α to be obtained inaccordance with ##EQU15## Here the values of the constant Q₁ (e.g.0.042) and the constant Q₂ (e.g. -0.128) have been set by statisticalprocessing. A step S303 calls for the program to return to a step S241of FIG. 3(E), at which SW3 is set to logical "1". The switch SW3 remainsat logical "1" from this point onward. In other words, the shapeparameter set at the early stage of temperature measurement is used fromnow on.

When SW3=1 is found to hold at the step S205 in FIG. 3(B), curveanalysis processing is executed at a step S400.

Curve Analysis Processing

FIG. 4(B) is a flowchart illustrating curve analysis processing inaccordance with the first embodiment of the invention. A step S401 callsfor the coefficient parameter A₀ to be obtained using the presentlyprevailing sensed temperature data T8, and for this value to be storedin the register A₀. The value of coefficient parameter A₀ is obtained inaccordance with the following equation: ##EQU16## Next, a step S402calls for the value of coefficient parameter A₁ to be obtained using thevalue of coefficient parameter A₀ obtained above, and for this value tobe stored in the register A₁. The value of coefficient parameter A₁ isobtained in accordance with the following equation:

    A.sub.1 =T.sub.A t.sub.A.sup.α -A.sub.0 t.sub.A.sup.α

The following step S403 calls for the value of coefficient parameter A₀' to be obtained using the sensed temperature data T₀ prevailing eightseconds earlier, and for this value to be stored in the register A₀ '.The value of coefficient parameter A₀ ' is obtained in accordance withthe following equation: ##EQU17## The following step S404 calls for thevalue of coefficient parameter A₁ ' to be obtained using the value ofcoefficient parameter A₀ ' obtained above, and for this value to bestored in the register A₁ '. The value of coefficient parameter A₁ ' isobtained in accordance with the following equation:

    A.sub.1 '=T.sub.A t.sub.A.sup.α -A.sub.0 't.sub.A.sup.α

The program then proceeds to a step S405, at which the program isreturned to a step S500 in order that prediction processing may beexecuted, as described below.

Prediction Processing

FIG. 4(C) is a flowchart illustrating prediction processing inaccordance with the first embodiment of the invention. A step S501 callsfor the predicted temperature T_(p) (t_(D)) at future time t_(D) (e.g.600 sec) to be obtained, and stored in the register T_(p), using thevalue of shape parameter α and the presently prevailing value ofcoefficient parameters A₀, A₁. The predicted temperature T_(p) (t_(D))is obtained in accordance with the following equation:

    T.sub.p (t.sub.D)=A.sub.0 +A.sub.1 /t.sub.D.sup.α

Next, at a step S502, the predicted temperature T_(p) (t_(D))' at futuretime t_(D) is obtained using the value of shape parameter α and thevalue of coefficient parameters A₀ ', A₁ ' prevailing eight secondsearlier. The predicted temperature T_(p) (t_(D))' is stored in theregister T_(p) '. The predicted temperature T_(p) (t_(D))' is obtainedin accordance with the following equation:

    T.sub.p (t.sub.D)'=A.sub.0 '+A.sub.1 '/t.sub.D.sup.α

A step S503 then calls for the program to return to a step S206, atwhich the predicted temperature T_(p) (t_(D)) obtained is displayed.

From the step S206 the program proceeds to a step S207 in the flowchartof FIG. 3(C), at which it is checked whether SW4=1 holds. SW4 is aswitch for processing in which the validity (consistency) of a predictedtemperature is examined. If SW=1 does not hold at the step S207, theprogram proceeds to a step S250, at which it is determined whether theabsolute value ↑T_(p) -T_(p) '↑ of the difference between temperaturespredicted at a time interval of eight seconds is equal to or less thanq. If ↑T_(p) -T_(p) '↑ does not hold, then the presently prevailingpredicted temperature T_(p) is not regarded as being approximately thesame at the predicted temperature T_(p) ' eight seconds earlier (i.e.the predicted temperature T_(p) is not valid). The program thereforereturns to the step S108 in the flowchart of FIG. 3(A). If ↑T_(p) -T_(p)'↑≦ q does hold, however, the prediction is construed to be stable andthe buzzer 14 is sounded at a step S251 to inform the user of the fact.The switch SW4 is then set to logical "1" at a step S252. The switch SW4remains at logical "1" from this point onward so that the buzzer 14 isnot sounded again. From this point onward the user is provided with avalid early display of temperature regardless of when the thermometer isremoved from contact with the body. If the user wishes to end thetemperature sensing operation early, the value being displayed at thistime can be recognized as being the sensed temperature value. However,if the body moves during measurement to bring about an unstablecondition in which a normal measurement cannot be taken, or if a slightfever is suspected and a more accurate prediction is required,temperature continues to be sensed. In such case, the program proceedsto the step S108 in FIG. 3(A). In response to the next timer interrupt,execution of the processing from the curve analysis step S400 to thepredicted temperature display step S206 in FIG. 3(B) is repeated. Thelonger measurement is continued, therefore, the more the accuracy of theprediction is raised. When the condition t_(i) >t_(M) (e.g. 600 sec) iseventually satisfied at the step S202, the sensed temperature data T_(O)itself approaches the equilibrium temperature. From this time onward,therefore, continuing the prediction processing becomes meaningless. Asa result, the program proceeds to a step S210, at which the dataselector 13 (FIG. 2(B)) is connected to terminal B. From this momentonward, the sensed temperature T_(O) per se is displayed.

FIGS. 8 through 10 are graphs illustrating the progress of bodytemperature measurement taken in an armpit using the electronic clinicalthermometer of the first embodiment. Elapsed measurement time t (min) isplotted along the horizontal axis, and temperature T (° C.) is plottedalong the vertical axis. The graphs show the transition of the sensedtemperature data T_(O), the transition of the predicted value T_(p)(600), namely the value predicted to prevail ten minutes (600 seconds)after the start of measurement, and the actually measured value T_(Omax)(600) ten minutes (600 seconds) after the start of measurement. "ERROR"in these graphs is the difference between predicted value T_(p) (600)and the actually measured value T_(Omax) (600) at the moment the buzzersounds (i.e. at the moment the prediction is construed to be valid)."JE" indicates an allowable value q (↑T_(p) -T_(p) '↑≦q) used in judgingthe validity of the prediction [executed at step S250 in FIG. 3(C)]. Ifthe prediction is valid, the buzzer is sounded.

FIG. 8 indicates a mean temperature rise curve. In accordance with FIG.8, the mean temperature rise curve is stable and the automatic settingof the value of shape parameter α is precise. Accordingly, the predictedvalue T_(p) (600) precisely represents the actually measured valueT_(Omax) (600) from the very moment the buzzer sounds. From this momentonward the transition is stable (approximately constant).

FIG. 9 illustrates a case where the temperature rise curve fluctuatesdue to body movement. Here the value of shape parameter α is set to0.154 and the temperature rise curve exhibits a gentler rise than thatof FIG. 8. In accordance with the invention, even in such cases as thatshown in FIG. 9, the initial predicted value T_(p) (600) that willprevail ten minutes after the start of measurement fluctuates onlyslightly in dependence upon the subsequent fluctuation of thetemperature rise curve. The reason for this is that since the value ofshape parameter α is set at an early stage of the temperaturemeasurement through a reliable method, the value of coefficientparameters A₀, A₁ fluctuate very little in dependence upon thesubsequent fluctuation of the temperature rise curve T_(O). As a result,the predicted value T_(p) (600) makes a stable transition. With theconventional method, the predicted value fluctuates widely in suchcases. FIG. 10 illustrates a case where the temperature rise curveexhibits a very gentle rise. In cases such as this, the predicted valuetends to be on the low side when the conventional prediction method isadopted. In accordance with the present embodiment, however, the valueof the shape parameter α is set to a somewhat lower value, as a resultof which the predicted value T_(p) (600) tends to rise somewhat moresteeply at the early stage of measurement and the buzzer activation timeis automatically delayed, whereby excellent prediction accuracy can beobtained from the moment the buzzer sounds. The reason for this is that,in accordance with the present invention, the value of the shapeparameter α is made to reflect the rising shape of the temperature risecurve, and the value of coefficient parameters A₀, A₁ of the predictionformula are set to values, which are based on actual measurement data,in accordance with the simultaneous equations.

Second Embodiment

In measurement of body temperature, the manner in which observedtemperature varies from the start of measurement until the attainment ofthermal equilibrium differs widely depending upon the thermalcharacteristics of the electronic clinical thermometer, the state of thepart of the body being measured and the characteristics thereof.However, if the thermal characteristics of the thermometer are limited,the manner in which temperature varies can be broken down into severalcategories, the largest whereof are armpit measurement, oral measurementand rectal measurement.

The electronic clinical thermometer of the first embodiment describedhereinabove can be applied as is to temperature measurement taken in anarmpit, mouth, rectum or other region by virtue of the universalprediction method characterizing that thermometer. However, if theprediction method is modified and limited solely to one type oftemperature measurement, such as measurement taken in an armpit, mouthor rectum, an improvement in the accuracy of temperature prediction canbe expected.

A characterizing feature of the second embodiment of the inventionresides in limiting body temperature measurement to e.g. armpitmeasurement, though measurement can be similarly limited to oral orrectal measurement. By doing so, the method of deciding the value ofshape parameter α is actualized more precisely than in the firstembodiment, thereby raising the accuracy of the prediction.

Another characteristic of the second embodiment is that the reliabilityof a predicted temperature is greatly improved by mitigating the effectsof a fluctuation in temperature change brought about during an actualmeasurement of body temperature.

A further characteristic of the second embodiment is that the processingload on the system CPU is lightened greatly by simplifying or reducingthe processing steps for predicting temperature.

Principle

If body temperature measurement is limited to measurement in e.g. anarmpit, then a correlation between a group of temperature change curvesand the value of shape parameter α can be stipulated more precisely. Asa result, the shape recognition means will be capable of recognizing theshape of a temperature change curve more precisely, thereby raising theaccuracy of the prediction processing.

FIG. 14 is a graph showing the relationship between an optimum value ofa shape parameter α and a variable (S₁ /S₂) for deciding the value ofshape parameter in the second embodiment of the invention. The graph isobtained in the following manner: First, a large number of sensedtemperature curves (preferably curves which are typical) indicative oftemperatures actually measured in the armpits of a large number ofpeople are prepared. Next, a given one of the sensed temperature curvesis selected and inputted to the electronic clinical thermometer. At suchtime the value of shape parameter α is set to and fixed at any value.The electronic clinical thermometer uses this value of α to obtain thevalue of coefficient parameters A₀, A₁ by solving the followingsimultaneous equations:

    T(t.sub.1)=A.sub.0 +A.sub.1 /t.sub.1.sup.α

    T(t.sub.2)=A.sub.0 +A.sub.1 /t.sub.2.sup.α

based on temperature data T(t₁), T(t₂) at two different points and timedata t₁, t₂ indicative of the corresponding points in time at which thetemperatures were sensed. Further, the values of the coefficientparameters A , A successively obtained are used to determine e.g. apredicted value T_(p) (600), which will prevail ten minutes hence, byusing the predictive arithmetic expression

    T.sub.p (t.sub.D)=A.sub.0 +A.sub.1 /t.sub.D.sup.α

When prescribed conditions indicating the end of a temperatureprediction [e.g. a state in which the predicted value T_(p) (600) hasstabilized] are satisfied, the predicted value T_(p) (600) at such timeand a known value T_(OA) (600) actually measured ten minutes after thestart of measurement are compared outside of the thermometer, whereby itis determined whether the prediction error γ=↑T_(p) (600)-T_(OA) (600)lies within a predetermined range. Next, the set value of the shapeparameter α is changed and the foregoing procedure is repeated. Theprocedure is carried out for all possible values of the shape parameterα, whereby there are obtained plural values of α (an α group) for whichthe prediction errors for a certain armpit temperature curve fall withinthe predetermined range.

Next, another armpit temperature curve is selected, the curve isinputted to the electronic clinical thermometer, and the above-describedprocedure is carried out, whereby there is obtained another α group forwhich the prediction errors for this other temperature curve fall withinthe predetermined range. The above procedure is repeated until all ofthe armpit temperature curves have been selected, thereby obtaining allgroups of α for which the prediction errors for all armpit temperaturecurves fall within the predetermined range.

Apart from the above, the shape variables (S₁ /S₂) for all of the armpittemperature curves are detected, and correspondence is establishedbetween the shape variable (S₁ /S₂) and the groups of α, using thetemperature curves as an intermediary. This is shown in the graph ofFIG. 14.

Next on the basis of the graph shown in FIG. 14, a relationship isestablished between the value of shape Parameter in the secondembodiment and the shape variable (S₁ /S₂). It will be apparent fromFIG. 14 that the shape of the graphed curve is an arc and not a straightline. The relation between α and (S₁ /S₂) is expressed in the form

    α=C.sub.1 (S.sub.1 /S.sub.2).sup.n +C.sub.2

or

    α=C.sub.1 (S.sub.1 /S.sub.2)+C.sub.2 (S.sub.1 /S.sub.2).sup.n +C.sub.3

where

n<1 : constant

C₁ -C₃ : constants

Accordingly, in the second embodiment, the relation between the value ofshape parameter α and the shape function (S₁ /S₂) is defined as follows:

    α=Q.sub.1 (S.sub.1 /S.sub.2)+Q.sub.2 (S.sub.1 /S.sub.2).sup.n +Q.sub.3(12)

where n < 1 : constant

Q₁ -Q₃ : constant

Each constant employed in the second embodiment is decided by regressivestatistical processing with respect to the graphical characteristic ofFIG. 14. For example, n=0.3, Q₁ =0.04467, Q₂ =-0.330749, Q₃ =0.393626.

Construction

FIGS. 11(A) and 11(B) are block diagrams illustrating in detail theconstruction of an electronic clinical thermometer according to thesecond embodiment, in which portions similar to those shown in FIGS.2(A) and 2(B) are designated by like reference characters. For the mostpart, these portions will not be described again unless they differslightly in terms of function.

In FIGS. 11(A) and 11(B), the prediction processing section 2 furtherincludes peak holding means 61 and averaging means 62. The shaperecognition means, curve analyzing means and prediction arithmetic meansare implemented in a way different from their counterparts in FIGS. 2(A)and 2(B) and are designated by numerals 20, 21, 22, respectively. Thecontrol means 8 causes the temperature measuring circuit 5 to generatethe temperature data T at a rate of once every four seconds prior to thestart of measurement (i.e. at the time of a preliminary measurement). Asa result, the preliminary measurement period is shortened in comparisonwith the first embodiment to raise the precision of preliminarymeasurement. Two seconds is stored as a constant in the register t_(A)of the measurement control means 8. The reason for this is so that thesensed temperature data T_(OA) which can be used first will becomeeffective two seconds after the start of measurement, owing to provisionof the averaging means 62, described below.

The peak holding means 61 constantly detects and stores the highesttemperature value contained in the sensed temperature data T_(O) read inby the data read-in means 6. T_(o) accomplish this, the peak holdingmeans 61 is provided with peak value memory means, not shown, forstoring at least one peak value T_(OP), and peak value comparing means,not shown, for comparing the magnitude of the peak value T_(OP) storedin the peak value storing means with a newly inputted item of sensedtemperature data T_(O). Initially, the peak value storing means storesthe sensed temperature data T_(O) prevailing at the start of measurement(or a set value T_(C) =30.0° C., which is set on the assumption thatmeasurement starting conditions have been satisfied) as the peak valueT_(OP). Next, when the latest item of sensed temperature data T_(O) isinputted, the peak value comparing means compares this item of data andthe peak value T_(OP) stored in the peak value memory means. When thecondition T_(O) ≧T_(OP) is satisfied, the peak value memory means storesthe new, i.e. latest, item of inputted temperature data T_(O).

As the temperature measurement operation proceeds, the averaging means62 determines a running average value T_(OA) of the peak values T_(OP)outputted by the peak holding means 61. To this end, the averaging means62 is provided with peak data memory means, not shown, for constantlystoring a predetermined number of consecutive peak values T_(OP) -T_(OP)', peak value adding means for adding the predetermined number of peakvalues T_(OP) -T_(OP) ' stored in the peak data memory means, anddividing means, not shown, for dividing the sum calculated in the peakvalue adding means by a predetermined number of peak values. The peakdata memory means is adapted to store the latest peak value T_(OP) ateach sampling instant and simultaneously erase the oldest peak valueT_(OP) ' already stored therein. By way of example, therefore, the peakdata storing means stores a peak value T_(OP0) at an initial samplinginstant (t=0 sec), the peak value T_(OP0) and a peak value T_(OP1) atthe next sampling instant (t=1 sec), and the peak values T_(OP0),T_(OP1) and a peak value T_(OP2) at the next sampling instant (t=2 sec).At this time (t=2sec), the peak value adding means outputs an initialsum T_(OS1) (=T_(OP0) +T_(OP1) +T_(OP2)), and the dividing meanscalculates and outputs an initial running average value T_(OA1)(=T_(OS1) /3). At the next sampling instant (t=3 sec), the peak datamemory means stores the peak values T_(OP1), T_(OP2), T_(OP3). As aresult, the peak value adding means outputs the next sum T_(OS2)(=T_(OP1) +T_(OP2) +T_(OP3)), and the dividing means calculates andoutputs the next running average value T_(OA2) (=T_(OS2) /3).

FIG. 13 is a graph showing the transitions of sensed temperature dataT_(O), peak data T_(OP) and running average value T_(OA) in the secondembodiment of the invention. The graph shows that the curve of sensedtemperature data T_(O) passes through e.g. 30.0° C. at the start ofmeasurement, thereafter rises monotonically until t=5 sec, at which thecurve temporarily descends before rising again at t=7 sec. Thisindicates sensed temperature curve fluctuation as caused by bodymovement during an actual measurement. In general, very smallfluctuations are removed by averaging processing. However, sensedtemperature data having the pronounced dip shown in FIG. 13 are notuseful in the prediction processing of the first and second embodimentsand, moreover, have an adverse effect upon the results of theprediction. Even in such cases, though, the peak holding means 61 storese.g. the peak value 30.5° C. at time t=5 sec and holds this value untiltime t=8 sec, so that the adverse influence due to the dip in the sensedtemperature data can be removed in the prediction processing.Furthermore, in accordance with the second embodiment, peak-holdprocessing is executed before the data averaging processing, so that theactual sensed temperature curve T_(O) and the peak value curve T_(OP)are in good agreement over the entire region of measurement. This meansthat an averaging error is excluded. The fact that an averaging error isexcluded is important at the early stage of measurement when the sensedtemperature curve is rising steeply and is advantageous in that thedelay in the rising shape of the running average value curve withrespect to the rising shape of the sensed temperature curve can bequantitatively evaluated when this is required. Next, by taking therunning average of the peak value curve T_(OP), the averaging means 62averages the fluctuation in the peak value curve T_(OP) to provide asmooth sensed temperature curve for subsequent processing in theprediction processing section 2.

The memory means 9 constantly stores a total of nine items of sensedtemperature data T8 through T0, including the most recent, whilesuccessively performing a shift-in/shift-out of the sensed temperaturedata T_(0A), indicative of the running average value obtained by theaveraging means 62, from register T8 to register T0 of the memory means.

By limiting measurement to that taken in e.g. an armpit, the shaperecognition means 20 is capable of obtaining a precise correlationbetween the value of shape parameter α and the variable (S₁ /S₂). Morespecifically, the register α in the shape recognition by performing thecalculation

    α=Q.sub.1 (S.sub.1 S.sub.2)+Q.sub.2 (S.sub.1 /S.sub.2).sup.0.3 +Q.sub.3

The shape recognition means 20 is additionally provided with a registerQ3 in order to store the constant Q₃.

The registers A₀ ', A₁ ' for storing the value of coefficient parametersA₀ ', A₁ ' prevailing eight seconds earlier are deleted from the curveanalyzing means 21, and the processing for determining the value ofcoefficient parameters A₀ ', A₁ ' is dispensed with. This is to lightenthe processing load on the processing section 2.

The prediction arithmetic means 22 is provided with predicted valuememory means for storing nine consecutive predicted values T_(p)(t_(D))-T_(p) (t_(D))'. The predicted value memory means is adapted toconstantly store the nine consecutive predicted values T_(p)(t_(D))-T_(p) (t_(D))' so as to store the latest predicted value T_(p)(t_(D)) calculated at each sampling instant and simultaneously erase theoldest predicted value T_(p) (t_(D))' already stored therein. Thus, theprediction arithmetic means 22 is relieved of the processing fordetermining the predicted value T_(p) (t_(D))' based on the temperaturedata prevailing eight seconds earlier. This greatly lightens theprocessing burden on the processing section 2. In this regard, theelectronic clinical thermometer of the first embodiment need not havethe stored value memory means of the second embodiment since it iscapable of effectively utilizing the temperature data T8-T0 in memorymeans 9.

Operation

FIGS. 12(A) through (C) are flowcharts illustrating shape recognitionprocessing, curve analysis processing and prediction processing inaccordance with the second embodiment. Since the main flow in the secondembodiment is arranged in the same manner as the main flow in FIGS. 3(A)through 3(E), the latter will also be employed in the description thatfollows. Accordingly, the overall operation of the second embodimentwill be described upon substituting shape recognition processing S300',curve analysis processing S400' and prediction processing S500' in FIGS.12(A)-(C) for the shape recognition processing S300, curve analysisprocessing S400 and prediction processing S500, respectively, in FIGS.3(B) and 3(E). Portions whose functions differ from those of the firstembodiment will be described.

In the preliminary measurement step S101 of FIG. 3(A), the measurementcontrol means 8 causes the temperature measurement circuit 5 to sensetemperature at a rate of once every four seconds. This is to shorten thepreliminary measurement period and raise the accuracy of the preliminarymeasurement. Step S102 calls for a determination as to whether apredetermined temperature of 30° C. has been exceeded, and step S103 adetermination as to whether the temperature rise is greater than 0.32°C. over the period of four seconds. In accordance with the secondembodiment, whether the temperature rise is greater than 0.32° C. ischecked in order to deal with the fact that the preliminary measurementperiod has been shortened to four seconds. The peak holding function ofthe peak holding means 61 and the averaging function of the averagingmeans 62 are also activated at step S107. The step S210 in FIG. 3(B)calls for a display of the sensed temperature data T_(OA) following theprocessing executed by the peak holding means 61 and averaging means 62.Elapsed time of two seconds is checked for at step S220, at which it isdetermined whether t_(i) =t_(A) holds. Since the averaging means 62 isprovided in the second embodiment, the content of register t_(A) istaken to be two seconds, which is the time at which the initial sensedtemperature T_(OA) can be used.

Shape Recognition Processing

Steps in FIG. 12(A) equivalent to those shown in FIG. 4(A) aredesignated by like step numbers and are not described again. A step S310in FIG. 12(A) calls for the value of shape parameter α to be found byusing the equation

    α=Q.sub.1 (S.sub.1 /S.sub.2)+Q.sub.2 (S.sub.1 /S.sub.2).sup.0.3 +Q.sub.3

This greatly raises the accuracy of shape recognition for temperaturemeasurement in an armpit.

In FIG. 12(B), the steps S403, S404 of FIG. 4(B) which use the sensedtemperature data T₀ prevailing eight seconds earlier are deleted. Sincethe prediction arithmetic means 22 in the second embodiment is providedwith the predicted value memory means capable of storing nine predictedvalues, it is unnecessary to obtain the value of coefficient parametersA₀ ', A₁ ' using the sensed temperature data T0 prevailing eight secondsprior to the present. This serves to lighten the processing burden onthe processing section 2 by a wide margin.

Prediction Processing

In FIG. 12(C), the steps equivalent to those shown in FIG. 4(C) aredesignated by like steps numbers and are not described again. Deletedfrom FIG. 12(C) is the step S502 of FIG. 4(C) which uses the sensedtemperature data T0 prevailing eight seconds earlier. This step isreplaced by a step S510, at which the latest predicted temperature T_(p)(t_(D)) is stored in the predicted value memory means and the predictedtemperature T_(p) (t_(D))', calculated and stored eight seconds earlier,is erased.

Third Embodiment

As set forth in the first embodiment, various other methods of settingthe value of shape parameter α can be conceived. A characterizingfeature of the third embodiment resides in actualizing, in greaterdetail, a method of expressing the value of shape parameter α bytemperature slopes at predetermined times after the start of measurementor plural items of temperature data at predetermined times after thestart of measurement, as touched upon in the description of the firstembodiment.

Another characterizing feature of the third embodiment resides inlimiting body temperature measurement to oral measurement, thoughmeasurement can be similarly limited to armpit or rectal measurement.Another method of deciding the value of shape parameter α is applied tothis measurement.

Another characterizing feature of the third embodiment is that theeffects of a fluctuation in temperature change are mitigated and theprocessing load on the processing section 2 is greatly reduced, as inthe second embodiment.

Principle

FIG. 17 is a graph showing a plot of plural items of sensed temperaturedata at predetermined times after the start of temperature measurementin accordance with the third embodiment. As set forth in the firstembodiment, a plurality of temperature rise curves can be distinguishedfrom one another by ascertaining the shapes of the sensed temperaturerises from the moment measurement starts. Accordingly, in the thirdembodiment of the invention, the value of shape parameter α is expressedbased on plural items of temperature data at predetermined points intime, as shown in FIG. 17.

There are several methods of ascertaining the sharpness of the shape ofsensed temperature rise. In one method, a set of actual measurement datais employed as is and is quantified or compared with a reference. Inanother, a set of actual measurement data is observed in relation to atime axis (i.e. is subjected to differentiation of the first order) toobtain information relating to the rate of temperature change, and theinformation is quantified or compared with a reference. In yet anotherconceivable method, a set of plural rates of temperature change takenfrom actual measurement data is observed in relation to a time axis(i.e. is subjected to differentiation of the second order) to obtaininformation relating to the acceleration of temperature change, and theinformation is quantified or compared with a reference. Whichever methodis used, it is desired that information which stipulates therelationship between the value of shape parameter and variables belinear, considering the processing capability of single-chip CPUspresently available. Fortunately, when a differential term is included,obtaining the linear relationship can be realized through simpleprocessing. Specifically, differentiation of the first order in e.g. theinterval t₈ -t₁₆ in FIG. 17 can be performed by employing the difference(T₁₆ -T₈) between the items of sensed temperature data prevailing atthese times, and quadratic differentiation in the interval t₈ -t₂₄ canbe performed in the form (T₂₄ -T₁₆ -(T₁₆ -T₈)). Accordingly, in thethird embodiment, the relation between the value of shape parameter αand variable X_(k) is defined as follows: ##EQU18## where D₀ -D₅ :constants

X₀ =T₈, X₁ =T₁₆

X₂ =T₂₄, X₃ =T₃₂

X₄ =(X₃ -X₀)/(X₁ -X₀)

In the above, T₈ -T₃₂ are items of sensed temperature data T_(OA) attimes t=8 sec, 16 sec, 24 sec and 32 sec, respectively, by way ofexample. The plural items of sensed temperature data T₈ -T₃₂ preferablyare sampled over a range covering a region in which the sharpnesses ofthe shapes of sensed rises in temperature after the start of measurementare effectively ascertained and distinguished from one another.Preferably, the range is made as short as possible in order to expressthe initial predicted temperature T_(p) at an earlier point in time.Further, the number of variables X₀ -X₄, the sampling interval and theshape preferably are decided within a range which will not burden theprocessing section 2 with excessive processing. The constants D₀ -D₅ inthe third embodiment are decided on the basis of the statisticalprocessing considered hereinabove. Examples of the constants are asfollows: D₀ =-0.02566, D₁ =0.01601, D₂ =0.03003, D₃ = 0.35019, D₄=0.08913, D₅ =-12.9657.

Construction

FIGS. 15(A) and 15(B) are block diagrams illustrating in detail theconstruction of an electronic clinical thermometer according to thethird embodiment, in which portions similar to those in the secondembodiment of FIG. 11 are designated by like reference characters andwill not be described again.

In FIGS. 15(A) and 15(B), the memory means 9 of FIG. 11(B) is deletedfrom the prediction processing section 2, which in this embodiment isprovided with measurement control means 23 and shape recognition means24 implemented in a way different from their counterparts in FIGS. 11(A)and 11(B).

In the measurement control means 23, the registers t₈ -t₃₂ storeconstants indicative of predetermined times at which the plural items oftemperature data are to be sampled. Examples of the constants are t₈ =8sec, t₁₆ =16 sec, t₂₄ =24 sec, t₃₂ =32 sec. It should be noted that thepredetermined constants are not limited to these alone. A register I isan index register. The contents of the register I index and refer to thecontents of the registers t₈ -t₃₂ and are used for other purposes aswell, as will be described below. By limiting measurement to e.g. oralmeasurement, the shape recognition means 24 is capable of obtaining amore precise correlation between the value of shape parameter α andvariable X_(k). More specifically, the register α of the shaperecognition means 24 stores the value of shape parameter α obtained byperforming the calculation expressed by the following equation:##EQU19## The shape recognition means 24 is provided with registers X₀-X₄ for storing variables X₀ -X₄, and with registers D₀ -D₅ for storingconstants D₀ -D₅.

Operation

FIGS. 16(A)-16(E) are flowcharts illustrating temperature sensingprocessing performed by the electronic clinical thermometer of the thirdembodiment. In FIG. 16(A), steps equivalent to those shown in FIG. 3(A)are designated by like step numbers and need not be described again.Steps having the same step numbers as those in the first embodiment butdiffering somewhat in terms of function will be described.

In the temperature measurement step S101 of FIG. 16(A), the measurementcontrol means 23 causes the temperature measurement circuit 5 to sensetemperature at a rate of once every four seconds. Step S102 calls for adetermination as to whether a predetermined temperature of 30° C. hasbeen exceeded, and step S103 a determination L- as to whether thetemperature rise is greater than 0.32° C. over the period of fourseconds. If YES answers are received at the steps S102, S103, then theprogram proceeds to a step S110, at which the various program switchesSW1-SW4 for measurement control and the contents of the index register Iare cleared. The peak holding function of the peak holding means 61 andthe averaging function of the averaging means 62 are activated at a stepS117.

When the timer interrupt is generated at the step S109 in FIG. 16(A),the program proceeds to the step S200 in FIG. 16(B). The sensedtemperature T_(OA) is displayed at the step S210 in FIG. 16(B). Elapsedtime of two seconds is checked for at the step S220, at which it isdetermined whether t_(i) =t_(A) holds. The sensed temperature dataT_(OA) is stored in the register T_(A) at the step S221. This is becausethe memory means 9 is not provided in the third embodiment.

It is checked at the step S205 whether SW3=1 holds. In the thirdembodiment, SW3 is a switch for storing plural items of temperature dataat predetermined points in time after the start of temperaturemeasurement, and for ascertaining the sharpness of the rising shape ofthe sensed temperature curve at a comparatively early point in timeafter the start of temperature measurement (i.e. for deciding the valueof shape parameter α). When SW3=1 does not hold, the program proceeds toa step S260 in FIG. 16(E), at which it is checked whether t_(i) is equalto any one of t₈, t₁₆, t₂₄, t₃₂. This decision step is executed whilethe contents of registers t₈ -t₃₂ in measurement control means 23 areindexed and referred to in accordance with the contents of the indexregister I. More specifically, initially I=0 and the system waits forthe condition t_(i) =8 sec to be established. When the condition t_(i)=8 sec does not hold, the data thus far are useless for shaperecognition, so that the program returns directly to the step S108. Whenthe condition t_(i) =8 sec is eventually established, an YES answer isreceived at the step S260 and the program proceeds to a step S261, atwhich the sensed temperature data T_(OA) (initially T₈) are stored inthe register X_(n) of shape recognition means 24 in accordance with thecontenss of the index register I. That is, since I=0 initially, T₈ isstored in the register X₀. Next, in accordance with the contents of theindex register I, and in a manner similar to the foregoing, the shaperecognition means 24 executes a partial calculation, namely α=f(I,X,D),for deciding the value of shape parameter at a step S262. That is, sinceI=0 initially, the partial calculation α =(D₀ X₀) is performed. Next, 1is added to the contents of the index register I at a step S262, and itis determined at a step S264 whether I=4 holds; if it does not, theprogram returns to the step S108. Thus, processing identical with theforegoing is executed successively at each of the predetermined pointsin time. In other words, since I=1 at the next predetermined time t_(i)=16 sec, T₁₆ is stored in register X₁ at the step S261, (D₁ X₁) is addedto the contents of the register α at the step S262, and 1 is added tothe contents of index register I at the step S263. Since I=2 at the nextpredetermined time t_(i) =24 sec, T₂₄ is stored in register X₂ at thestep S261, (D₂ X₂) is added to the contents of the register α at thestep S262, and 1 is added to the contents of index register I at thestep S263. Since I=3 at the next predetermined time t_(i) =32 sec, T₃₂is stored in register X₃ at the step S261, (D₃ X₃) is added to thecontents of the register at the step S262, and 1 is added to thecontents of index register I at the step S263. At this moment thecondition I=4 is found to hold at a decision step S264, so that shaperecognition processing of step S600, described below, is executed. Inaccordance with the third embodiment of the invention, this moment isalways a point in time t=32 sec after the start of measurement.

Shape Recognition Processing

FIG. 16(D) is a flowchart illustrating shape recognition processing inaccordance with the third embodiment of the invention. A step S601 callsfor the variable X₄ =(X₃ -X₀)/(X₁ -X₀) to be stored in register X₄.Next, the remainder (D₄ X₄ +D₅) is added to the contents of the registerα at a step S602. The value of shape parameter α is thus calculated. Itis then determined at a step S603 whether α>1 holds. If the latter doeshold, α is clamped at 1 at a step S606; if it does not hold, then it isdetermined whether α< 0.01 holds at a step S604. If the latter doeshold, α is clamped at 0.01 at a step S605; if it does not hold, then thevalue of α obtained by the calculation at step S602 is used as is. Theprogram returns to the main flow at a step S607. The switch SW3 it setto logical "1" at a step S265 [FIG. 16(E)]. This step S265 is notexecuted again. From this point onward, in other words, the predictioncalculations continue, using the value of shape parameterα set early inthe body temperature measurement operation.

FIGS. 18 and 19 are graphs showing temperature sensed in an armpitplotted against elapsed measurement time in the electronic clinicalthermometer of the second embodiment. FIG. 18 illustrates a case wherethe temperature rise curve ascends very gently, and FIG. 19 illustratesan average temperature rise curve. In the electronic clinicalthermometer of the second embodiment, the value of the shape parameter αis decided at the moment the shape of a predetermined shoulder portionof the temperature rise curve is detected. Therefore, the moment atwhich the buzzer is sounded also varies in dependence upon a change inthe rising shape of the temperature rise curve.

FIGS. 20 through 22 are graphs showing orally sensed temperature plottedagainst elapsed measurement time in the electronic clinical thermometerof the third embodiment. FIG. 20 shows an average temperature risecurve, FIG. 21 depicts a case where a temperature higher than that ofFIG. 20 is measured, and FIG. 22 illustrates a case where thetemperature rise curve ascends very gently. In the electronic clinicalthermometer of the third embodiment, the value of the shape parameter αis always decided at time t=32 sec, so that the moment at which thebuzzer sounds does not change much.

Though the foregoing embodiments relate to an electronic clinicalthermometer used to measure the temperature of the human body, theinvention or the concept thereof can readily be applied to thetemperature measurement of other living bodies or to objects other thanliving bodies.

In the second and third embodiments of the invention, the invention isdescribed in connection with electronic clinical thermometers adaptedspecifically for armpit and oral measurement, respectively. However,none of the measurement algorithms (especially the algorithms fordeciding the value of shape parameter α) are limited to armpit or oraluse. In addition, measurement algorithms for armpit and oral measurementcan both be incorporated in one and the same electronic clinicalthermometer, which would be provided with a function enabling either ofthe two algorithms to be selected at will.

Furthermore, in the first and second embodiments, the comparison betweenthe second and third slopes S₁, S₂, respectively, is expressed in termsof their ratio. However, the comparison is not limited to a ratio butcan also be expressed as, say, the difference between these two slopes.

ADVANTAGES OF THE INVENTION

In accordance with the present invention as set forth hereinabove, allparameters in the prediction formula are calculated using real-timetemperature data when a measurement is taken. This makes it possible toobtain an accurate, early display of temperature at all times even ifsensed temperature curves differ because of a dispersion in the thermalcharacteristics of the probe, individual differences or differences inthe part of a body where temperature is sensed.

Further, since real-time temperature data per se are used as purposivevariables, there is no adverse influence ascribable to calculationerror, parameters can be set stably, and predicted values do notfluctuate widely even when noise is superimposed on an actually measuredtemperature curve.

Moreover, temperature data are sampled as measurement proceeds in such amanner that all temperature rise curves are covered. Accordingly, evenif a temperature rise curve ascends very gently, the transition of thepredicted value describes a natural rise curve and there is no overshootin the vicinity of temperature rise. This makes it possible for ameasurement to be taken without the user being aware of the fact that aprediction is being made.

Since the shape of the temperature rise curve is correctly judged earlyin the temperature measurement operation, the predicted temperaturevalue indicates the equilibrium temperature from the first.

Since any future time can be directly set with regard to the predictionformula, a sensed temperature value which prevails after any elapsedmeasurement time can be provided with ease. This also makes it possibleto provide a predicted value of thermal equilibrium temperature whichwill prevail in the future after a very long elapsed time period.

Further, in accordance with the invention, simultaneous equations withtwo unknowns are solved after the value of curve shape α is set.Therefore, temperature data at one point can be fixed to temperaturedata in the vicinity of measurement start, so that it will suffice iftemperature data at one other point use temperature data prevailing atthe present point in time.

This makes it possible to dispense with a temperature data memoryrequired in the prior art, and to speed up and simplify processing.Accordingly, an inexpensive, highly accurate electronic clinicalthermometer having a high degree of universality can be provided.

In accordance with the invention, fluctuation in a temperature changecurve caused by e.g. movement of the living body can be effectivelyprevented or alleviated, thereby enhancing the reliability of apredicted temperature.

Since measurement can be limited to a specific part of a living body,the accuracy of a prediction can be greatly improved.

What is claimed is:
 1. A method of measuring temperature of a livingbody, wherein sensed temperature at a future time is capable of beingpredicted based on a predictive functional formula having elapsed timefrom the start of temperature measurement as variable, said methodcomprising the steps of:providing a predictive functional formula havingtemperature data as independent variables and elapsed time data asdependent variables and including a shape parameter for reflecting theshape of a sensed temperature curve and coefficient parameters forsuperimposing said predictive functional formula on said sensedtemperature curve, the value of said shape parameter and coefficientparameters being unknown; a temperature sensing step of sensingtemperature and generating temperature data indicative of thetemperature sensed; a time measuring step of measuring elapsed time fromstart of temperature measurement and generating time data indicative ofthe measured elapsed time; setting a value of a shape parameter on thebasis of said temperature data and said elapsed time data obtainedearly. setting said value of coefficient parameters by solving asimultaneous equation comprising a plurality of said predictivefunctional formula, said plurality of predictive functional formulasbeing solved by substituting for said formula variables said value ofshape parameter, temperature data, and elapsed time data at a pluralityof different points in time; and calculating sensed temperature at afuture time in accordance with the predictive functional formulaspecified by said value of shape parameter and coefficient parameters.2. The method according to claim 1, wherein said predictive functionalformula is

    T(t)=A.sub.0 +A.sub.1 /t.sup.α

where A₀ and A₁ : coefficient parameters α: shape parameter t: elapsedtime from the start of temperature measurement, and (T(t) : temperatureon elapsed time t.
 3. The method of according to claim 2, wherein saidvalue of shape parameter α is set on the basis of temperature riseinformation obtained from a plurality of temperature data.
 4. The methodaccording to claim 3, wherein said value of shape parameter is set bydetecting a point at which the sensed temperature curve exhibits a firstpredetermined slope, detecting a second slope S₁ preceding the detectedpoint and a third slope S₂ following the detected point, and comparingsaid second and third slopes.
 5. The method according to claim 4,wherein said value of shape parameter α is set on the basis of saidsecond slope S₁ and third slope S₂, in accordance with the followingequation:

    α=Q.sub.1 (S.sub.1 /S.sub.2)+Q.sub.2 (S.sub.1 /S.sub.2).sup.n +Q.sub.3

where n (a constant)<1 Q₁ -Q₃ : constants
 6. The method according toclaim 2, wherein said value of shape parameter is set on the basis ofplural items of temperature data at an early stage of temperaturemeasurement following the start of measurement.
 7. The method accordingto claim 6, wherein said value of shape parameter α is set on the basisof information X_(k) based on plural items of temperature data T_(k) atrespective predetermined points in time, in accordance with thefollowing equation: ##EQU20## where D₀ -D₅ constantsX₀ -X₃ : T₀ -T₃ X₄=(X₃ -X₀)/(X₁ -X₀)
 8. The method according to claim 1, wherein saidvalue of coefficient parameters A₀, A₁ are set by solving the followingsimultaneous equations with two unknowns:

    T(t.sub.1)=A.sub.0 +A.sub.1 /t.sub.1.sup.α

    T(t.sub.2)=A.sub.0 +A.sub.1 /t.sub.2.sup.α

on the basis of temperature data T(t₁), T(t₂) at two different points intime and time data t₁, t₂ respectively indicative of the points in timeat which the temperature is sensed.
 9. The method according to claim 8,wherein said temperature data at the two different points in time aretemperature data in the vicinity of measurement starting time andtemperature data at a present point in time.
 10. The method according toclaim 1, wherein said sensed temperature T_(p) (t_(D)) at a future timet_(D) is calculated in accordance with the following equation:

    T.sub.p (t.sub.D)=A.sub.0 +A.sub.1 /t.sub.D.sup.α

based on a prediction function specified by the value of shape parameterα and coefficient parameters A₀, A₁.
 11. An apparatus for measuring thetemperature of a living body, wherein sensed temperature at a futuretime is capable of being predicted, comprising:memory means for storinga predictive functional formula having temperature data as independentvariables and elapsed time data as dependent variable and including ashape parameter for reflecting the shape of a sensed temperature curveand coefficient parameters for superimposing said predictive functionalformula on said sensed temperature curve, wherein said shape parameterand coefficient parameters are unknown; temperature sensing means forsensing temperature and generating temperature data indicative of thetemperature sensed; time measuring means for measuring elapsed time fromstart of temperature measurement and generating time data indicative ofthe measured elapsed time; shape parameter setting means for setting avalue of shape parameter on the basis of said temperature data andelapsed time data; coefficient parameter setting means for setting avalue of coefficient parameters by solving a simultaneous equationcomprising a plurality of said predictive functional formula, saidplurality of predictive functional formulas being solved by substitutingfor said formula variables said value of shape parameter, andtemperature data and elapsed time data at a plurality of differentpoints in time; and prediction processing means for calculating sensedtemperature at a future time in accordance with the predictivefunctional formula specified by said value of shape parameter andcoefficient parameters.
 12. The apparatus according to claim 11, whereinsaid temperature sensing means includes peak holding means forsuccessively detecting peak levels of sensed temperature and for holdingand outputting the detected peak levels.
 13. The apparatus according toclaim 11, wherein said temperature sensing means includes peak holdingmeans for successively detecting peak levels of temperature sensed at apredetermined period and for holding and outputting the detected peaklevels, and averaging means for obtaining and outputting a runningaverage value of plural peak levels held by said peak holding means. 14.The apparatus according to claim 11, wherein said shape parametersetting means sets the value of shape parameter on the basis ofpredetermined temperature rise slope information, which is based onplural items of temperature data.
 15. The apparatus according to claim11, wherein said shape parameter setting means sets the value of shapeparameter on the basis of plural items of temperature data at an earlystage of temperature measurement following start of measurement.
 16. Theapparatus according to claim 11, wherein said coefficient parametersetting means sets the value of coefficient parameters A₀, A₁ by solvingthe following simultaneous equations with two unknowns:

    T(t.sub.1)=A.sub.0 +A.sub.1 /t.sub.1.sup.α

    T(t.sub.2)=A.sub.0 +A.sub.1 /t.sub.2.sup.α

on the basis of temperature data T(t₁), T(t₂) at two different points intime and time data t₁, t₂ respectively indicative of the points in timeat which temperature is sensed.
 17. The apparatus according to claim 16,wherein said coefficient parameter setting means includes using, as thetemperature data at the two different points in time, temperature datain the vicinity of measurement starting time and temperature data at apresent point in time.
 18. The apparatus according to claim 11, whereinsaid prediction processing means calculates a sensed temperature T_(p)(t_(D)), which will prevail at a future time t_(D), in accordance withthe following equation:T_(p) (t_(D))=A₀ +A₁ /t_(D).sup.α based on aprediction function specified by the value of shape parameter α andcoefficient parameters A₀, A₁.